Fostering the use of reflective learning strategies in teaching big data analytics and data science: Enhancing the quality of epidemiological evidence and decision-making

Abstract

PURPOSEThis study investigated the frequency of use and perceived effectiveness of reflective learning strategies among university educators and practitioners in big data analytics and data science, including epidemiologists. Reflective learning fosters critical thinking and problem-solving skills essential for navigating the complexities of big data. It encourages systematic evaluation of methodologies and the identification and mitigation of biases. These skills are particularly relevant in epidemiology, where the prediction and management of disease outbreaks, as well as public health decisions, increasingly depend on big data analytics. METHODOLOGYA cross-sectional design was employed with a diverse sample of 93 participants from multiple countries, averaging 15 years of teaching and practice experience. Informed by the Diffusion of Innovation Theory and the Theory of Planned Behavior, data were collected in Fall 2023 using an online questionnaire. The questionnaire focused on personal disposition, contextual favorability, and characteristics of reflective learning as predictors of the frequency and perceived effectiveness of reflective learning, analyzed using multiple regression modeling. Acceptable levels of reliability and criterion validity were achieved. This study was approved by the Ethics Panel of Canterbury Christ Church University, UK. RESULTSIn general, reflective learning strategies were perceived as effective for enhancing cognitive skills, such as critical thinking and problem-solving, but less so for technical skills like algorithm and model selection. Preferred methods included real-world applications, problem-based learning, and collaborative projects. Frequency of use was positively correlated with perceived effectiveness. Personal disposition and characteristics of reflective learning scores were significant predictors of frequency of use (R-squared = 0.32), with older participants being less engaged. The characteristics of reflective learning score was the sole significant predictor of perceived effectiveness, though with a modest predictive value (R-squared = 0.09). Contextual favorability did not significantly predict either frequency of use or perceived effectiveness of reflective learning strategies, implying a lesser role for external factors as drivers of engagement. CONCLUSIONSThis study underscores the importance of reflective learning in enhancing analytical and problem-solving skills, and suggests that intrinsic motivation may be a key factor underpinning the use of reflective learning strategies by educators and practitioners in big data analytics and data science. These findings have major implications for curriculum and professional development, particularly for epidemiologists who must continuously adapt to new data and methodologies to effectively address public health challenges.